Modelling of Virus Spread Using Information from Biased Testing

Dr Fanel Donea1

1CSIRO – Scientific Computing, Melbourne, Australia

This work aims to contribute to the fight against viruses by understanding how they may spread in reality, considering the limited information available from biased testing.

Non-medical methods (mathematical, computational, simulational) are a necessary supplement to direct medical approaches, especially for new viruses, such as covid-19, for which the medical research isstill incipient.

A combination of practical methods is used, including Monte Carlo simulations, agent-based modelling and analytic techniques, based on the classical SIR (susceptible-infected-removed) model. An immediate goal is to use establish an estimate of the real number of cases, in the situation where testing has not been randomised. The same techniques can then be applied in the future for conceptual proofs or disproofs for various claims that have been circulating in the media (effects of ignoring quarantine, the possibility of achieving herd immunity, the effectiveness of mass testing and others).


Biography:

Fanel Donea is a scientific software engineer in the Modelling and Data group of CSIRO’s Scientific Computing department. His current interests are in the fields of computational modelling and quantum computing. In a previous life, he worked in astrophysics, and¬† he obtained a PhD in Physics, studying accretion discs around black holes.

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